With the ongoing proliferation of data acquisition devices, more and more physiological aspects are able to be captured as electrophysiological signals. Some examples include, but are not limited to, gamma synchrony signals (based on electroencephalogram (EEG) measurements), a respiratory function signal, a pulse oximetry signal (measuring the oxygenation of a patient's blood), a perfusion data signal (measuring changes in tissue images following introduction of a contrast agent to the blood), and quasi-periodic biological signals.
Devices which capture electrophysiological signals may be valuable tools for physicians to study the health conditions of a patient. After the recording of the electrophysiological signal, it is up to the physician or healthcare provider to perform the signal analysis. For example, in the case of ECG signal analysis, there are certain integrated automatic analysis processes and systems which automatically determine different types of heart beats, rhythms, etc. The traditional output from the existing ECG software is basic data that often needs to be supplemented by an angiography or arteriography, Cardiac MRI (Magnetic Resonance Imaging), CT (Computed tomography) or a more invasive test. However, there are a number of limitations associated with all such systems described above, they are complex, their outputs are difficult to analyze, and such techniques are expensive to use.
In addition to the above systems, there are various time domain and frequency domain signal processing techniques which are being used for the analysis of electrophysiological signals to obtain more detailed information. Unfortunately, the time domain techniques are incapable of quantifying certain fluctuation characteristics of a number of pathologies related to the electrophysiological signal. For example, with regard to the heart, traditional methods for performing frequency-domain analysis of surface ECG signals, such as the Fourier transform, are limited since they do not address the beat-to-beat multi-lead variability in the morphology and phase of the entire ECG cycle over long consecutive time windows and the random nature of biological and electromagnetic noise or the variation between patients.
For example, in case of arrhythmia, the heart generates very complex ECG waveforms that have a large variation in morphologies. Dominant frequency analysis on these ECGs can be problematic since non-linear dynamic systems can appear to generate random noise. Discrete fast Fourier transforms and wavelet analysis have been shown experimentally to be incapable of detecting deterministic chaos in the presence of strong periodicity which tends to obscure the underlying non-linear structures. Thus, the detection of complex sub-harmonic frequencies which are thought to exist in all arrhythmia requires dynamic non-linear analyses. Complex subharmonic frequencies are similarly thought to exist in other types of electrophysiological signals and may be indicative of other pathological events which are not otherwise detectable from the electrophysiological signal using prior art methods.